Manners used to make a man, now they make a loan. Trolling or stalking someone online can crimp your chances of getting a personal loan, and decide whether you get it at 30% interest or 9%, as against the industry rate of 13-17%.

New-age online lenders like InstaPaisa, GoPaySense, Faircent, CashCare and Vote4Cash, and online credit marketplaces like CreditMantri and BankBazaar.com that have found a clientele in the 25-35 age group do their due diligence on borrowers using not just payslips and bank statements but also unorthodox metrics like phone location data, SMS alerts and social media behaviour.

Their algorithms can run through tonnes of data in minutes to assign a personality score, which is the measure of a borrower's reliability.

While online marketplaces like CreditMantri and BankBazaar.com act as facilitators for consumers to access bank loans at competitive rates, app-based lenders tie up with an NBFC to provide loans. (Both InstaPaisa, CASHe said they were working towards getting their own NBFC licence to operate independently.)

"We can look at the history of Google keywords and websites visited; our algorithms run on sentiment analytics. Emotions like anger and people raging on Twitter do get captured," said Nikhil Sama, CEO of InstaPaisa.

Does an applicant live beyond their means, drive drunk, gamble or indulge in other high-risk behaviour? The algorithms can spot it all. You live in a world of information overload. Your social media footprint can leave an indelible mark on your credit history, said V Raman Kumar, founder of CASHe.

Such analyses have also made it easy to measure the creditworthiness of non-salaried people like lawyers, freelancers and consultants who may be high earners otherwise. Banks and other traditional lenders are usually chary of lending to them.

"While these people have fat bank accounts, they might not necessarily get loans because they are non-salaried. So we look at mobile usage pattern, GPS location and SMS alerts. We can also verify PAN, Aadhaar database, date of birth and use of their registered mobile phone number. We look at traditional data sets before non-traditional metrics," said Sama.

At EarlySalary.com, which gives loans for even 1-7 days, GPS coordinates, and Facebook and LinkedIn data are used to build a machine scorecard. "We once rejected a loan application from a person with two credit card defaults. Within a few minutes, his girlfriend (confirmed from Facebook) applied from the same GPS location. We noticed she transferred money regularly from her bank account to her boyfriend's account," said Akshay Mehrotra, co-founder of EarlySalary.com. Social media is also a handy identity verification tool now.

Online lenders consider an applicant's Facebook followers and LinkedIn connections. "How many likes or comments you get for what you post, how long you have used this medium; all this information gets fed into our systems and a black box decision algorithm takes the final call on lending," said Sama, adding, "We just want to make sure you are a real person with a credible profile."

Some players tie up with e-commerce sites and telecom providers to find out whether a customer pays phone bills on time or is a frequent defaulter. "We have some data pooled in from our e-commerce partners and a few telecom players," said Vikas Sekhri, founder of CashCare.

The deep access third parties have to user data certainly seems invasive, but at some point we have all signed away the information while installing apps and subscribing to services. Does it mean some lender could be remotely scanning the contents of your phone — maybe texts from a sweetheart — right now?

"Just like Ola, Uber, Swiggy, other mobile apps request permission before accessing mobile data, we also get the consumer's permission to access mobile content. Our algorithms don't read or scan personal messages.The only SMSes that would get picked up are the transaction alerts that banks and e-wallets send. And none of this information is seen by a human eye," said Sekhar. "Our systems' artificial intelligence picks up this data, analyses it and then comes to a decision. Data does not get stored with us and is used only to arrive at a decision."